Abstract The continuing goal of our research program is to optimize and disseminate effective imaging-based strategies to personalize brain tumor treatment. Current Response Assessment in NeuroOncology (RANO) criteria, which incorporate anatomic imaging only, are insufficient for distinguishing tumor from treatment effect (TE). Without definitive confirmation of tumor progression, no treatment changes are recommended for several months after standard therapies. Thus, patients are precluded from switching to potentially more effective therapies?a limitation that could be overcome with more reliable imaging techniques. To this end, during the previous funding cycle, we demonstrated the feasibility of several quantitative imaging (QI) tools to reliably distinguish tumor from treatment effect and predict treatment response. These QI tools include a machine-learning approach to calibrate T1w images enabling the creation of quantitative delta T1 (qDT1) maps. The qDT1 enable the detection of true contrast enhancing lesion volume (CELV). The qDT1 together with our proven dynamic susceptibility contrast (DSC) MRI methods, for determination of rCBV (relative cerebral blood volume), are used to generate a new biomarker, fractional tumor burden (FTB), to delineate the extent of tumor within CELV on a voxel-wise basis. These perfusion-based QI tools in combination with our diffusion MRI technology, which includes functional diffusion maps (FDMs) and more recently RSI (restriction spectrum imaging), provide a comprehensive assessment of brain tumor and its distinction from treatment effect. Now, in order to translate this technology for use in clinical trials and daily practice, some final updates and clinical validation studies are needed as proposed here. First, to ease adoption and testing in the clinical setting improvements are proposed for the individual QI technologies along with the development of a streamlined workflow (Aim 1). To improve the widespread adoption of DSC-MRI and FTB biomarker, studies will be performed to confirm that a single-dose DSC-MRI method can replace the standard double-dose method without affecting the accuracy of rCBV or the creation of FTB maps (Aim 1.1). Also, registration and segmentation algorithms will be updated to include deformable registration and recent advances in deep learning for longitudinal reporting of CELV, non-enhancing lesion volumes (NELV) and each of the QI metrics (Aim 1.2). Finally, a streamlined workflow that incorporates these improvements will be created (Aim 1.3). The Aim 2 studies will test the QI tools and workflow using clinical trial data (Aim 2.1-2.2) and daily clinical practice (Aim 2.3-2.4). Clinical validation of this new QI-RANO workflow, with evidence showing improved prediction in comparison to current measures, has the potential to cause a paradigm shift in how brain tumor burden is assessed.